'''MobileNetV2 in PyTorch.

See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F

from .deconv import *

class Block(nn.Module):
    '''expand + depthwise + pointwise'''
    def __init__(self, in_planes, out_planes, expansion, stride, deconv=None):
        super(Block, self).__init__()
        self.stride = stride

        planes = expansion * in_planes
        if deconv:
            self.deconv=True
            self.conv1 = deconv(in_planes, planes, kernel_size=1, stride=1, padding=0, bias=True)
            self.conv2 = deconv(planes, planes, kernel_size=3, stride=stride, padding=1, groups=planes, bias=True,n_iter=1)
            self.conv3 = deconv(planes, out_planes, kernel_size=1, stride=1, padding=0, bias=True,block=in_planes,n_iter=1)

            self.shortcut = nn.Sequential()
            if stride == 1 and in_planes != out_planes:
                self.shortcut = nn.Sequential(
                    deconv(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=True)
                )

        else:
            self.deconv=False
            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, stride=1, padding=0, bias=False)
            self.bn1 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, groups=planes, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)
            self.conv3 = nn.Conv2d(planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
            self.bn3 = nn.BatchNorm2d(out_planes)

            self.shortcut = nn.Sequential()
            if stride == 1 and in_planes != out_planes:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False),
                    nn.BatchNorm2d(out_planes),
                )

    def forward(self, x):
        if self.deconv:
            out = F.relu(self.conv1(x))
            out = F.relu(self.conv2(out))
            out = self.conv3(out)
        else:
            out = F.relu(self.bn1(self.conv1(x)))
            out = F.relu(self.bn2(self.conv2(out)))
            out = self.bn3(self.conv3(out))
        out = out + self.shortcut(x) if self.stride==1 else out
        return out


class MobileNetV2(nn.Module):
    # (expansion, out_planes, num_blocks, stride)
    cfg = [(1,  16, 1, 1),
           (6,  24, 2, 1),  # NOTE: change stride 2 -> 1 for CIFAR10
           (6,  32, 3, 2),
           (6,  64, 4, 2),
           (6,  96, 3, 1),
           (6, 160, 3, 2),
           (6, 320, 1, 1)]

    def __init__(self, num_classes=10, deconv=None,delinear=None,channel_deconv=None):
        super(MobileNetV2, self).__init__()
        # NOTE: change conv1 stride 2 -> 1 for CIFAR10
        if deconv:
            self.deconv=True
            self.conv1 = deconv(3, 32, kernel_size=3, stride=1, padding=1, bias=True,freeze=True,n_iter=10)
            self.conv2 = deconv(320, 1280, kernel_size=1, stride=1, padding=0, bias=True)
            
        else:
            self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
            self.bn1 = nn.BatchNorm2d(32)
            self.conv2 = nn.Conv2d(320, 1280, kernel_size=1, stride=1, padding=0, bias=False)
            self.bn2 = nn.BatchNorm2d(1280)

        self.layers = self._make_layers(in_planes=32, deconv=deconv)
        
        if delinear:
            self.linear = delinear(1280, num_classes)
        else:
            self.linear = nn.Linear(1280, num_classes)
        
        if channel_deconv:
            self.deconv1=channel_deconv()
    def _make_layers(self, in_planes, deconv=None):
        layers = []
        for expansion, out_planes, num_blocks, stride in self.cfg:
            strides = [stride] + [1]*(num_blocks-1)
            for stride in strides:
                layers.append(Block(in_planes, out_planes, expansion, stride, deconv=deconv))
                in_planes = out_planes
        return nn.Sequential(*layers)

    def forward(self, x):
        if hasattr(self,'bn1'):
            out = F.relu(self.bn1(self.conv1(x)))
        else:
            out = F.relu(self.conv1(x))

        out = self.layers(out)
        if hasattr(self,'bn2'):
            out = F.relu(self.bn2(self.conv2(out)))
        else:
            out = F.relu(self.conv2(out))
        # NOTE: change pooling kernel_size 7 -> 4 for CIFAR10
        if hasattr(self, 'deconv1'):
            out = self.deconv1(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def test():
    net = MobileNetV2()
    x = torch.randn(2,3,32,32)
    y = net(x)
    print(y.size())

# test()
